Successive Collaborative SLAM for Professional Use Cases: Map Learning Aspects

Kurzfassung

Pedestrian navigation solely based on inertial measurement units in indoor environments is challenging if there is no signal of opportunity that can be exploited. In collaborative simultaneous localization and mapping (collaborative SLAM) the learned maps of several users can be combined in order to help indoor positioning. In this paper, we investigate the combination of the maps of several pedestrians and analyze the resulting position accuracy and the quality of the map. Depending on the number of users, on the sensor drifts, and on the amount of revisited area the quality of the maps varies. This paper provides a discussion about the map learning aspects and the advantages of successive collaborative mapping when there is no loop closure. The results show that even without loop closures the position error can be reduced to ~ 0.5m when applying successive collaborative mapping.